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Merge pull request #387 from influxdata/flux-0.38 

Flux 0.38
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15
content/v2.0/reference/flux/functions/built-in/transformations/aggregates/aggregatewindow.md
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@ -10,7 +10,8 @@ menu:
weight: 501
---
The `aggregateWindow()` function applies an aggregate function to fixed windows of time.
The `aggregateWindow()` function applies an aggregate or selector function
(any function with a `column` parameter) to fixed windows of time.
_**Function type:** Aggregate_
@ -25,7 +26,7 @@ aggregateWindow(
)
```
As data is windowed into separate tables and aggregated, the `_time` column is dropped from each group key.
As data is windowed into separate tables and processed, the `_time` column is dropped from each group key.
This function copies the timestamp from a remaining column into the `_time` column.
View the [function definition](#function-definition).
@ -42,7 +43,7 @@ The [aggregate function](/v2.0/reference/flux/functions/built-in/transformations
_**Data type:** Function_
{{% note %}}
Only aggregate functions with a `column` parameter (singular) work with `aggregateWindow()`.
Only aggregate and selector functions with a `column` parameter (singular) work with `aggregateWindow()`.
{{% /note %}}
### column
@ -84,10 +85,10 @@ from(bucket: "example-bucket")
fn: mean
)
```
###### Specifying parameters of the aggregate function
To use `aggregateWindow()` aggregate functions that don't provide defaults for required parameters,
for the `fn` parameter, define an anonymous function with `columns` and `tables` parameters
that pipe-forwards tables into the aggregate function with all required parameters defined:
###### Specify parameters of the aggregate function
To use functions that don't provide defaults for required parameters with `aggregateWindow()`,
define an anonymous function with `column` and `tables` parameters that pipe-forward
tables into the aggregate or selector function with all required parameters defined:
```js
from(bucket: "example-bucket")

27
content/v2.0/reference/flux/functions/built-in/transformations/aggregates/difference.md
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@ -17,7 +17,11 @@ _**Function type:** Aggregate_
_**Output data type:** Float_
```js
difference(nonNegative: false, column: "_value")
difference(
nonNegative: false,
column: "_value",
keepFirst: false
)
```
## Parameters
@ -34,6 +38,13 @@ Defaults to `"_value"`.
_**Data type:** String_
### keepFirst
Indicates the first row should be kept.
If `true`, the difference will be `null`.
Defaults to `false`.
_**Data type:** Boolean_
## Subtraction rules for numeric types
- The difference between two non-null values is their algebraic difference;
or `null`, if the result is negative and `nonNegative: true`;
@ -90,6 +101,20 @@ from(bucket: "example-bucket")
| 0004 | 6 | tv |
| 0005 | null | tv |
#### With keepFirst set to true
```js
|> difference(nonNegative: false, keepfirst: true):
```
###### Output table
| _time | _value | tag |
|:-----:|:------:|:---:|
| 0001 | null | tv |
| 0002 | null | tv |
| 0003 | -2 | tv |
| 0004 | 6 | tv |
| 0005 | null | tv |
<hr style="margin-top:4rem"/>
##### Related InfluxQL functions and statements:

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content/v2.0/reference/flux/functions/built-in/transformations/aggregates/doubleema.md Normal file
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---
title: doubleEMA() function
description: >
The `doubleEMA()` function calculates the exponential moving average of values
grouped into `n` number of points, giving more weight to recent data at double
the rate of `exponentialMovingAverage()`.
menu:
v2_0_ref:
name: doubleEMA
parent: built-in-aggregates
weight: 501
related:
- /v2.0/reference/flux/functions/built-in/transformations/aggregates/movingaverage/
- /v2.0/reference/flux/functions/built-in/transformations/aggregates/tripleema/
- /v2.0/reference/flux/functions/built-in/transformations/aggregates/timedmovingaverage/
- /v2.0/reference/flux/functions/built-in/transformations/aggregates/exponentialmovingaverage/
- https://docs.influxdata.com/influxdb/v1.7/query_language/functions/#double-exponential-moving-average, InfluxQL DOUBLE_EXPONENTIAL_MOVING_AVERAGE()
---
The `doubleEMA()` function calculates the exponential moving average of values in
the `_value` column grouped into `n` number of points, giving more weight to recent
data at double the rate of [`exponentialMovingAverage()`](/v2.0/reference/flux/functions/built-in/transformations/aggregates/exponentialmovingaverage/).
_**Function type:** Aggregate_
```js
doubleEMA(n: 5)
```
##### Double exponential moving average rules
- A double exponential moving average is defined as `doubleEMA = 2 * EMA_N - EMA of EMA_N`.
- `EMA` is an exponential moving average.
- `N = n` is the period used to calculate the EMA.
- A true double exponential moving average requires at least `2 * n - 1` values.
If not enough values exist to calculate the double EMA, it returns a `NaN` value.
- `doubleEMA()` inherits all [exponential moving average rules](/v2.0/reference/flux/functions/built-in/transformations/aggregates/exponentialmovingaverage/#exponential-moving-average-rules).
## Parameters
### n
The number of points to average.
_**Data type:** Integer_
## Examples
#### Calculate a five point double exponential moving average
```js
from(bucket: "example-bucket"):
|> range(start: -12h)
|> doubleEMA(n: 5)
```
## Function definition
```js
doubleEMA = (n, tables=<-) =>
tables
|> exponentialMovingAverage(n:n)
|> duplicate(column:"_value", as:"ema")
|> exponentialMovingAverage(n:n)
|> map(fn: (r) => ({r with _value: 2.0 * r.ema - r._value}))
|> drop(columns: ["ema"])
```

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content/v2.0/reference/flux/functions/built-in/transformations/aggregates/exponentialmovingaverage.md
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@ -1,8 +1,8 @@
---
title: exponentialMovingAverage() function
description: >
The `exponentialMovingAverage()` function calculates the exponential moving average
of values grouped into `n` number of points, giving more weight to recent data.
The `exponentialMovingAverage()` function calculates the exponential moving average of values
in the `_value` column grouped into `n` number of points, giving more weight to recent data.
menu:
v2_0_ref:
name: exponentialMovingAverage
@ -11,22 +11,21 @@ weight: 501
related:
- /v2.0/reference/flux/functions/built-in/transformations/aggregates/movingaverage/
- /v2.0/reference/flux/functions/built-in/transformations/aggregates/timedmovingaverage/
- /v2.0/reference/flux/functions/built-in/transformations/aggregates/doubleema/
- /v2.0/reference/flux/functions/built-in/transformations/aggregates/tripleema/
- https://docs.influxdata.com/influxdb/v1.7/query_language/functions/#exponential-moving-average, InfluxQL EXPONENTIAL_MOVING_AVERAGE()
---
The `exponentialMovingAverage()` function calculates the exponential moving average
of values grouped into `n` number of points, giving more weight to recent data.
The `exponentialMovingAverage()` function calculates the exponential moving average of values
in the `_value` column grouped into `n` number of points, giving more weight to recent data.
_**Function type:** Aggregate_
```js
exponentialMovingAverage(
n: 5,
columns: ["_value"]
)
exponentialMovingAverage(n: 5)
```
##### Exponential moving average rules:
##### Exponential moving average rules
- The first value of an exponential moving average over `n` values is the
algebraic mean of `n` values.
- Subsequent values are calculated as `y(t) = x(t) * k + y(t-1) * (1 - k)`, where:
@ -43,11 +42,6 @@ The number of points to average.
_**Data type:** Integer_
### columns
Columns to operate on. _Defaults to `["_value"]`_.
_**Data type:** Array of Strings_
## Examples
#### Calculate a five point exponential moving average
@ -60,23 +54,20 @@ from(bucket: "example-bucket"):
#### Table transformation with a two point exponential moving average
###### Input table:
| _time | A | B | C | tag |
|:-----:|:----:|:----:|:----:|:---:|
| 0001 | 2 | null | 2 | tv |
| 0002 | null | 10 | 4 | tv |
| 0003 | 8 | 20 | 5 | tv |
| _time | tag | _value |
|:-----:|:---:|:------:|
| 0001 | tv | null |
| 0002 | tv | 10 |
| 0003 | tv | 20 |
###### Query:
```js
// ...
|> exponentialMovingAverage(
n: 2,
columns: ["A", "B", "C"]
)
|> exponentialMovingAverage(n: 2)
```
###### Output table:
| _time | A | B | C | tag |
|:-----:|:----:|:----:|:----:|:---:|
| 0002 | 2 | 10 | 3 | tv |
| 0003 | 6 | 16.67| 4.33 | tv |
| _time | tag | _value |
|:-----:|:---:|:------:|
| 0002 | tv | 10 |
| 0003 | tv | 16.67 |

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---
title: holtWinters() function
description: >
The `holtWinters()` function applies the Holt-Winters forecasting method to input tables.
aliases:
- /v2.0/reference/flux/functions/transformations/aggregates/holtwinters
menu:
v2_0_ref:
name: holtWinters
parent: built-in-aggregates
weight: 501
related:
- https://docs.influxdata.com/influxdb/latest/query_language/functions/#holt-winters, InfluxQL HOLT_WINTERS()
---
The `holtWinters()` function applies the Holt-Winters forecasting method to input tables.
_**Function type:** Aggregate_
_**Output data type:** Float_
```js
holtWinters(
n: 10,
seasonality: 4,
interval: 30d,
withFit: false,
timeColumn: "_time",
column: "_value",
)
```
The Holt-Winters method predicts [`n`](#n) seasonally-adjusted values for the
specified [`column`](#column) at the specified [`interval`](#interval).
For example, if `interval` is `6m` and `n` is `3`, results include three predicted
values six minutes apart.
#### Seasonality
[`seasonality`](#seasonality) delimits the length of a seasonal pattern according to `interval`.
If your `interval` is `2m` and `seasonality` is `4`, then the seasonal pattern occurs every
eight minutes or every four data points.
If data doesn't have a seasonal pattern, set `seasonality` to `0`.
#### Space values evenly in time
`holtWinters()` expects values evenly spaced in time.
To ensure `holtWinters()` values are spaced evenly in time, the following rules apply:
- Data is grouped into time-based "buckets" determined by the `interval`.
- If a bucket includes many values, the first value is used.
- If a bucket includes no values, a missing value (`null`) is added for that bucket.
By default, `holtWinters()` uses the first value in each time bucket to run the Holt-Winters calculation.
To specify other values to use in the calculation, use:
- [`window()`](/v2.0/reference/flux/functions/built-in/transformations/window/)
with [selectors](/v2.0/reference/flux/functions/built-in/transformations/selectors/)
or [aggregates](/v2.0/reference/flux/functions/built-in/transformations/aggregates/)
- [`aggregateWindow()`](/v2.0/reference/flux/functions/built-in/transformations/aggregates/aggregatewindow)
#### Fitted model
The `holtWinters()` function applies the [Nelder-Mead optimization](https://en.wikipedia.org/wiki/Nelder%E2%80%93Mead_method)
to include "fitted" data points in results when [`withFit`](#withfit) is set to `true`.
#### Null timestamps
`holtWinters()` discards rows with `null` timestamps before running the Holt-Winters calculation.
#### Null values
`holtWinters()` treats `null` values as missing data points and includes them in the Holt-Winters calculation.
## Parameters
### n
The number of values to predict.
_**Data type: Integer**_
### seasonality
The number of points in a season.
Defaults to `0`.
_**Data type: Integer**_
### interval
The interval between two data points.
_**Data type: Duration**_
### withFit
Return [fitted data](#fitted-model) in results.
Defaults to `false`.
_**Data type: Boolean**_
### timeColumn
The time column to use.
Defaults to `"_time"`.
_**Data type: String**_
### column
The column to operate on.
Defaults to `"_value"`.
_**Data type: String**_
## Examples
##### Use aggregateWindow to prepare data for holtWinters
```js
from(bucket: "example-bucket")
|> range(start: -7y)
|> filter(fn: (r) => r._field == "water_level")
|> aggregateWindow(every: 379m, fn: first).
|> holtWinters(n: 10, seasonality: 4, interval: 379m)
```

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content/v2.0/reference/flux/functions/built-in/transformations/aggregates/movingaverage.md
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@ -10,21 +10,21 @@ weight: 501
related:
- /v2.0/reference/flux/functions/built-in/transformations/aggregates/timedmovingaverage/
- /v2.0/reference/flux/functions/built-in/transformations/aggregates/exponentialmovingaverage/
- /v2.0/reference/flux/functions/built-in/transformations/aggregates/doubleema/
- /v2.0/reference/flux/functions/built-in/transformations/aggregates/tripleema/
- https://docs.influxdata.com/influxdb/latest/query_language/functions/#moving-average, InfluxQL MOVING_AVERAGE()
---
The `movingAverage()` function calculates the mean of values grouped into `n` number of points.
The `movingAverage()` function calculates the mean of values in the `_values` column
grouped into `n` number of points.
_**Function type:** Aggregate_
```js
movingAverage(
n: 5,
columns: ["_value"]
)
movingAverage(n: 5)
```
##### Moving average rules:
##### Moving average rules
- The average over a period populated by `n` values is equal to their algebraic mean.
- The average over a period populated by only `null` values is `null`.
- Moving averages skip `null` values.
@ -38,11 +38,6 @@ The number of points to average.
_**Data type:** Integer_
### columns
Columns to operate on. _Defaults to `["_value"]`_.
_**Data type:** Array of Strings_
## Examples
#### Calculate a five point moving average
@ -52,36 +47,23 @@ from(bucket: "example-bucket"):
|> movingAverage(n: 5)
```
#### Calculate a ten point moving average
```js
movingAverage = (every, period, column="_value", tables=<-) =>
tables
|> window(every: every, period: period)
|> mean(column: column)
|> duplicate(column: "_stop", as: "_time")
|> window(every: inf)
```
#### Table transformation with a two point moving average
###### Input table:
| _time | A | B | C | D | tag |
|:-----:|:----:|:----:|:----:|:----:|:---:|
| 0001 | null | 1 | 2 | null | tv |
| 0002 | 6 | 2 | null | null | tv |
| 0003 | 4 | null | 4 | 4 | tv |
| _time | tag | _value |
|:-----:|:---:|:------:|
| 0001 | tv | null |
| 0002 | tv | 6 |
| 0003 | tv | 4 |
###### Query:
```js
// ...
|> movingAverage(
n: 2,
columns: ["A", "B", "C", "D"]
)
|> movingAverage(n: 2 )
```
###### Output table:
| _time | A | B | C | D | tag |
|:-----:|:----:|:----:|:----:|:----:|:---:|
| 0002 | 6 | 1.5 | 2 | null | tv |
| 0003 | 5 | 2 | 4 | 4 | tv |
| _time | tag | _value |
|:-----:|:---:|:------:|
| 0002 | tv | 6 |
| 0003 | tv | 5 |

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---
title: relativeStrengthIndex() function
description: >
The `relativeStrengthIndex()` function measures the relative speed and change of
values in an input table.
menu:
v2_0_ref:
name: relativeStrengthIndex
parent: built-in-aggregates
weight: 501
related:
- /v2.0/reference/flux/functions/built-in/transformations/aggregates/movingaverage/
- /v2.0/reference/flux/functions/built-in/transformations/aggregates/timedmovingaverage/
- /v2.0/reference/flux/functions/built-in/transformations/aggregates/exponentialmovingaverage/
- https://docs.influxdata.com/influxdb/v1.7/query_language/functions/#relative-strength-index, InfluxQL RELATIVE_STRENGTH_INDEX()
---
The `relativeStrengthIndex()` function measures the relative speed and change of
values in an input table.
_**Function type:** Aggregate_
```js
relativeStrengthIndex(
n: 5,
columns: ["_value"]
)
```
##### Relative strength index rules
- The general equation for calculating a relative strength index (RSI) is
`RSI = 100 - (100 / (1 + (AVG GAIN / AVG LOSS)))`.
- For the first value of the RSI, `AVG GAIN` and `AVG LOSS` are averages of the `n` period.
- For subsequent calculations:
- `AVG GAIN` = `((PREVIOUS AVG GAIN) * (n - 1)) / n`
- `AVG LOSS` = `((PREVIOUS AVG LOSS) * (n - 1)) / n`
- `relativeStrengthIndex()` ignores `null` values.
## Parameters
### n
The number of values to use to calculate the RSI.
_**Data type:** Integer_
### columns
Columns to operate on. _Defaults to `["_value"]`_.
_**Data type:** Array of Strings_
## Examples
#### Calculate a five point relative strength index
```js
from(bucket: "example-bucket"):
|> range(start: -12h)
|> relativeStrengthIndex(n: 5)
```
#### Table transformation with a ten point RSI
###### Input table:
| _time | A | B | tag |
|:-----:|:----:|:----:|:---:|
| 0001 | 1 | 1 | tv |
| 0002 | 2 | 2 | tv |
| 0003 | 3 | 3 | tv |
| 0004 | 4 | 4 | tv |
| 0005 | 5 | 5 | tv |
| 0006 | 6 | 6 | tv |
| 0007 | 7 | 7 | tv |
| 0008 | 8 | 8 | tv |
| 0009 | 9 | 9 | tv |
| 0010 | 10 | 10 | tv |
| 0011 | 11 | 11 | tv |
| 0012 | 12 | 12 | tv |
| 0013 | 13 | 13 | tv |
| 0014 | 14 | 14 | tv |
| 0015 | 15 | 15 | tv |
| 0016 | 16 | 16 | tv |
| 0017 | 17 | null | tv |
| 0018 | 18 | 17 | tv |
###### Query:
```js
// ...
|> relativeStrengthIndex(
n: 10,
columns: ["A", "B"]
)
```
###### Output table:
| _time | A | B | tag |
|:-----:|:----:|:----:|:---:|
| 0011 | 100 | 100 | tv |
| 0012 | 100 | 100 | tv |
| 0013 | 100 | 100 | tv |
| 0014 | 100 | 100 | tv |
| 0015 | 100 | 100 | tv |
| 0016 | 90 | 90 | tv |
| 0017 | 81 | 90 | tv |
| 0018 | 72.9 | 81 | tv |

2
content/v2.0/reference/flux/functions/built-in/transformations/aggregates/timedmovingaverage.md
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related:
- /v2.0/reference/flux/functions/built-in/transformations/aggregates/movingaverage/
- /v2.0/reference/flux/functions/built-in/transformations/aggregates/exponentialmovingaverage/
- /v2.0/reference/flux/functions/built-in/transformations/aggregates/doubleema/
- /v2.0/reference/flux/functions/built-in/transformations/aggregates/tripleema/
- https://docs.influxdata.com/influxdb/latest/query_language/functions/#moving-average, InfluxQL MOVING_AVERAGE()
---

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---
title: tripleEMA() function
description: >
The `tripleEMA()` function calculates the exponential moving average of values
grouped into `n` number of points, giving more weight to recent data with less lag
than `exponentialMovingAverage()` and `doubleEMA()`.
menu:
v2_0_ref:
name: tripleEMA
parent: built-in-aggregates
weight: 501
related:
- /v2.0/reference/flux/functions/built-in/transformations/aggregates/movingaverage/
- /v2.0/reference/flux/functions/built-in/transformations/aggregates/doubleema/
- /v2.0/reference/flux/functions/built-in/transformations/aggregates/timedmovingaverage/
- /v2.0/reference/flux/functions/built-in/transformations/aggregates/exponentialmovingaverage/
- https://docs.influxdata.com/influxdb/v1.7/query_language/functions/#triple-exponential-moving-average, InfluxQL TRIPLE_EXPONENTIAL_MOVING_AVERAGE()
---
The `tripleEMA()` function calculates the exponential moving average of values in
the `_value` column grouped into `n` number of points, giving more weight to recent
data with less lag than
[`exponentialMovingAverage()`](/v2.0/reference/flux/functions/built-in/transformations/aggregates/exponentialmovingaverage/)
and [`doubleEMA()`](/v2.0/reference/flux/functions/built-in/transformations/aggregates/doubleema/).
_**Function type:** Aggregate_
```js
tripleEMA(n: 5)
```
##### Triple exponential moving average rules
- A triple exponential moving average is defined as `tripleEMA = (3 * EMA_1) - (3 * EMA_2) + EMA_3`.
- `EMA_1` is the exponential moving average of the original data.
- `EMA_2` is the exponential moving average of `EMA_1`.
- `EMA_3` is the exponential moving average of `EMA_2`.
- A true triple exponential moving average requires at least requires at least `3 * n - 2` values.
If not enough values exist to calculate the triple EMA, it returns a `NaN` value.
- `tripleEMA()` inherits all [exponential moving average rules](/v2.0/reference/flux/functions/built-in/transformations/aggregates/exponentialmovingaverage/#exponential-moving-average-rules).
## Parameters
### n
The number of points to average.
_**Data type:** Integer_
## Examples
#### Calculate a five point triple exponential moving average
```js
from(bucket: "example-bucket"):
|> range(start: -12h)
|> tripleEMA(n: 5)
```
## Function definition
```js
tripleEMA = (n, tables=<-) =>
tables
|> exponentialMovingAverage(n:n)
|> duplicate(column:"_value", as:"ema1")
|> exponentialMovingAverage(n:n)
|> duplicate(column:"_value", as:"ema2")
|> exponentialMovingAverage(n:n)
|> map(fn: (r) => ({r with _value: 3.0 * r.ema1 - 3.0 * r.ema2 + r._value}))
|> drop(columns: ["ema1", "ema2"])
```

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---
title: date.microsecond() function
description: >
The `date.microsecond()` function returns the microsecond of a specified time.
Results range from `[0-999999]`.
menu:
v2_0_ref:
name: date.microsecond
parent: Date
weight: 301
---
The `date.microsecond()` function returns the microsecond of a specified time.
Results range from `[0-999999]`.
_**Function type:** Transformation_
```js
import "date"
date.microsecond(t: 2019-07-17T12:05:21.012934584Z)
// Returns 12934
```
## Parameters
### t
The time to operate on.
_**Data type:** Time_

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---
title: date.millisecond() function
description: >
The `date.millisecond()` function returns the millisecond of a specified time.
Results range from `[0-999999]`.
menu:
v2_0_ref:
name: date.millisecond
parent: Date
weight: 301
---
The `date.millisecond()` function returns the millisecond of a specified time.
Results range from `[0-999]`.
_**Function type:** Transformation_
```js
import "date"
date.millisecond(t: 2019-07-17T12:05:21.012934584Z)
// Returns 12
```
## Parameters
### t
The time to operate on.
_**Data type:** Time_

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---
title: date.nanosecond() function
description: >
The `date.nanosecond()` function returns the nanosecond of a specified time.
Results range from `[0-999999999]`.
menu:
v2_0_ref:
name: date.nanosecond
parent: Date
weight: 301
---
The `date.nanosecond()` function returns the nanosecond of a specified time.
Results range from `[0-999999999]`.
_**Function type:** Transformation_
```js
import "date"
date.nanosecond(t: 2019-07-17T12:05:21.012934584Z)
// Returns 12934584
```
## Parameters
### t
The time to operate on.
_**Data type:** Time_

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---
title: date.quarter() function
description: >
The `date.quarter()` function returns the quarter of the year for a specified time.
Results range from `[1-4]`.
menu:
v2_0_ref:
name: date.quarter
parent: Date
weight: 301
---
The `date.quarter()` function returns the quarter of the year for a specified time.
Results range from `[1-4]`.
_**Function type:** Transformation_
```js
import "date"
date.quarter(t: 2019-07-17T12:05:21.012Z)
// Returns 3
```
## Parameters
### t
The time to operate on.
_**Data type:** Time_

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---
title: date.truncate() function
description: >
The `date.truncate()` function truncates a time to a specified unit.
menu:
v2_0_ref:
name: date.truncate
parent: Date
weight: 301
---
The `date.truncate()` function truncates a time to a specified unit.
_**Function type:** Transformation_
```js
import "date"
date.truncate(
t: 2019-07-17T12:05:21.012Z
unit: 1s
)
// Returns 2019-07-17T12:05:21.000000000Z
```
## Parameters
### t
The time to operate on.
_**Data type:** Time_
### unit
The unit time to truncate to.
_**Data type:** Duration_
{{% note %}}
Only use `1` and the unit of time to specify the `unit`.
For example: `1s`, `1m`, `1h`.
{{% /note %}}
## Examples
```js
import "date"
date.truncate(t: "2019-06-03T13:59:01.000000000Z", unit: 1s)
// Returns 2019-06-03T13:59:01.000000000Z
date.truncate(t: "2019-06-03T13:59:01.000000000Z", unit: 1m)
// Returns 2019-06-03T13:59:00.000000000Z
date.truncate(t: "2019-06-03T13:59:01.000000000Z", unit: 1h)
// Returns 2019-06-03T13:00:00.000000000Z
```

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---
title: date.week() function
description: >
The `date.week()` function returns the ISO week of the year for a specified time.
Results range from `[1-53]`.
menu:
v2_0_ref:
name: date.week
parent: Date
weight: 301
---
The `date.week()` function returns the ISO week of the year for a specified time.
Results range from `[1-53]`.
_**Function type:** Transformation_
```js
import "date"
date.week(t: 2019-07-17T12:05:21.012Z)
// Returns 29
```
## Parameters
### t
The time to operate on.
_**Data type:** Time_

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---
title: date.year() function
description: >
The `date.year()` function returns the year of a specified time.
menu:
v2_0_ref:
name: date.year
parent: Date
weight: 301
draft: true
---
The `date.year()` function returns the year of a specified time.
_**Function type:** Transformation_
```js
import "date"
date.year(t: 2019-07-17T12:05:21.012Z)
// Returns 2019
```
## Parameters
### t
The time to operate on.
_**Data type:** Time_

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---
title: Flux runtime package
list_title: Runtime package
description: >
The Flux runtime package includes functions that provide information about the
current Flux runtime. Import the `runtime` package.
menu:
v2_0_ref:
name: Runtime
parent: Flux packages and functions
weight: 202
v2.0/tags: [runtime, functions, package]
---
The Flux runtime package includes functions that provide information about the
current Flux runtime. Import the `runtime` package:
```js
import "runtime"
```
{{< children type="functions" show="pages" >}}

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---
title: runtime.version() function
description: The `runtime.version()` function returns the current Flux version.
menu:
v2_0_ref:
name: runtime.version
parent: Runtime
weight: 401
---
The `runtime.version()` function returns the current Flux version.
_**Function type:** Miscellaneous_
_**Output data type:** String_
```js
import "runtime"
runtime.version()
```

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@ -11,13 +11,37 @@ aliases:
---
{{% note %}}
_The latest release of InfluxDB v2.0 alpha includes **Flux v0.37.2**.
_The latest release of InfluxDB v2.0 alpha includes **Flux v0.38.0**.
Though newer versions of Flux may be available, they will not be included with
InfluxDB until the next InfluxDB v2.0 release._
{{% /note %}}
---
## v0.38.0 [2019-08-06]
### Features
- Update selectors to operate on time columns.
- Add `relativeStrengthIndex()` transformation.
- Add double and triple exponential average transformations (`doubleEMA()` and `tripleEMA()`).
- Add `holtWinters()` transformation.
- Add `keepFirst` parameter to `difference()`.
- DatePart equivalent functions.
- Add runtime package.
- Add and subtract duration literal arithmetic.
- Allow `keep()` to run regardless of nonexistent columns.
If all columns given are nonexistent, `keep()` returns an empty table.
- Scanner returns positioning.
### Bug fixes
- Function resolver now keeps track of local assignments that may be evaluated at runtime.
- Fixed InfluxDB test errors.
- Add range to tests to pass in InfluxDB.
- Allow converting a duration to a duration.
- Catch integer overflow and underflow for literals.
---
## v0.37.2 [2019-07-24]
- _General cleanup of internal code._